Navid Bayati is an Associate Professor at the University of Southern Denmark, affiliated with the Institute of Mechanical and Electrical Engineering and the Centre for Industrial Electronics. He leads the Control and Protection of Smart Grids (CAP-SG) group and focuses on renewable/hybrid power systems, microgrid protection, and grid code compliance. Education: Ph.D. in Power Systems & Microgrid Protection (2020, Aalborg University); M.Sc. in Power Systems (2017, Amirkabir University of Technology) His research spans renewable energy integration , transient analysis , grid interconnection , and digital twin applications . Recent work includes machine learning for carbon emission prediction, fault localization in DC microgrids, and supercapacitor resilience in hybrid systems. Collaborations include projects like IEA Wind Task 50 and RePoSys , addressing grid renovation, life cycle assessment, and digital twin resilience. His teaching portfolio covers power electronics , energy management , and microgrid control .
Liadh Kelly is an Assistant Professor in the Department of Computer Science at Maynooth University's Faculty of Science & Engineering. She supervises PhD students in applied artificial intelligence, focusing on intelligent search, ubiquitous computing, and multimodal information access. She is affiliated with the ADAPT SFI Research Centre, SFI Centre for Research Training in Foundations of Data Science, and Human Health Institute. BSc in Computer Science MSc (Research) in Computer Science PhD in Computer Science Her research explores context-sensitive retrieval and evaluation methodology in AI-driven systems. Key areas include ubiquitous computing for personal data analysis, deep learning classification for mental wellness indicators, and multimodal lifelogging integration. Recent publications focus on urban mental wellbeing classification , contextual cue analysis , and AI-driven health search systems . Articles address smart city applications , consumer health search , and cross-lingual medical retrieval . Professional roles include Doctoral Consortium Chair at ECIR 2023 and Programme Committee member for SIGIR and ICWSM conferences. She leads grants for 4-year PhD studentships with stipend and fee coverage.
Ngoc Thanh Nguyen is a Full Professor at Wroclaw University of Science and Technology where he serves as Head of the Department of Applied Informatics. He holds the prestigious title of Professor granted by the President of Poland and has been recognized as a Distinguished Scientist of ACM since 2009. He serves as Editor-in-Chief of both the Journal of Information and Telecommunication (JIT) and the Vietnam Journal of Computer Science (VJCS), and chairs the IEEE SMC Technical Committee on Computational Collective Intelligence. His research spans computational collective intelligence, knowledge integration, data mining, social media analysis, and sentiment analysis. Professor Nguyen has pioneered significant methodologies in spatial data clustering within network space, inter-sequence pattern mining, and graph neural network applications. His work bridges theoretical computer science with practical applications in intelligent information systems, demonstrating particular expertise in handling complex spatial and sequential data structures. His research has evolved from foundational pattern mining techniques to sophisticated neural network approaches for geospatial and social data analysis. The analysis of his recent publications reveals a strong focus on spatial data analysis in network environments, with significant contributions to clustering algorithms, graph neural networks, and pattern mining. His work consistently addresses efficiency challenges in data processing while expanding into emerging areas like Vietnamese language processing and topological data analysis. The research demonstrates a clear trajectory from traditional data mining techniques toward more sophisticated AI-driven approaches that incorporate spatial relationships and network topologies. Distinguished Scientist of ACM (2009) ACM Distinguished Speaker (2009-2013) IEEE Distinguished Visitor (2009-2013) Title of Professor granted by the President of Poland Professor Nguyen has supervised over 20 PhD students to completion and currently mentors several ongoing doctoral candidates. His academic leadership extends to founding two major conference series: the Asian Conference on Intelligent Information and Database Systems (ACIIDS) and the International Conference on Computational Collective Intelligence (ICCCI), which have become significant venues in their respective fields. His collaborative network spans multiple institutions, particularly with Yeungnam University as evidenced by several co-supervised PhD projects. As founder and chair of the IEEE SMC Technical Committee on Computational Collective Intelligence, he leads an international community of researchers advancing this specialized field. His departmental leadership at Wroclaw University of Science and Technology positions him at the center of applied informatics research and education in Poland, with particular emphasis on computational intelligence applications.
Anthony TUNG Kum Hoe is a Professor in the Department of Computer Science at the National University of Singapore (NUS), where he has established himself as a leading researcher in database systems and data mining. He is also affiliated with the NUS Graduate School for Integrative Sciences and Engineering and serves as a SINGA supervisor. His educational background includes a Ph.D. in Computer Science from Simon Fraser University (2001), an M.Sc. in Information Systems & Computer Science from NUS (1998), and a B.Sc. with 2nd Class Upper Honours in Information Systems & Computer Science from NUS (1997). Professor Tung's research spans several interconnected areas within database systems and data mining. His primary focus is on developing efficient methods for indexing and searching complex data structures including time series, trajectories, trees, graphs, and high-dimensional objects. He has pioneered work in visual query processing, keyword search, and ranking systems. His GENIE (Generic Inverted Index) and LAMP (semi-Lazy Mining Paradigm) projects represent significant contributions to big data analytics, particularly in handling the 'variety' aspect of big data by providing unified frameworks for processing diverse data structures while preserving semantic meaning. His research bridges theoretical database concepts with practical applications in visual data mining, collaborative analytics, and just-in-time model construction. His recent publications reveal a clear evolution from traditional database research toward more complex analytics on diverse data types. While maintaining his core expertise in database indexing and query processing, his work has expanded to incorporate machine learning techniques, particularly in areas like nearest neighbor search, anomaly detection, and predictive analytics. There's a noticeable trend toward interdisciplinary applications, with publications spanning computer vision, natural language processing, transportation systems, and social computing. His research group consistently publishes in top-tier venues including SIGMOD, VLDB, ICDE, and KDD, demonstrating both theoretical rigor and practical relevance. 2005 Best Paper Award for 'Indexing DNA Sequences Using q-grams' 2007 Invited panel speaker on 'Advice for a successful database researcher career in Asia' at SIGMOD 2010 Guest Lecturer for VLDB Database School 2012 VLDB 2012 Research PC Co-chairs 2015 10 Years Best Paper Award, DASFAA 2015 Invited to SIGMOD 2008 and SIGKDD 2008 Program Committees Professor Tung has supervised numerous PhD students and research associates throughout his career, including notable researchers like Zhang Zhenjie (recipient of the 2007 President Graduate Fellowship) and Wang Nan (published in SIGMOD'08). His research group has been consistently productive, with students publishing in top conferences including SIGMOD, ICDE, and VLDB. His professional service is extensive, having served as PC Chair for COMAD'06, Research PC Co-chair for VLDB 2012, and on program committees for virtually all major database and data mining conferences over the past two decades. His research has been supported by various grants that have enabled significant contributions to database technology. His GENIE and LAMP projects represent a cohesive research direction focused on developing systematic approaches to big data analytics. GENIE provides a unified platform for storage and retrieval of big data with various structures, while LAMP introduces a novel paradigm for predictive analytics that combines the strengths of lazy and eager learning approaches. These projects have evolved to incorporate GPU acceleration and parallel processing capabilities, reflecting his commitment to addressing real-world scalability challenges in data-intensive applications.
Angelo Castaldo is an Associate Professor in Public Finance at the Faculty of Law, Sapienza University of Rome, with a Ph.D. in Law and Economics from the University of Siena and an M.Sc. in Economics from the University of York. He serves as Chair of the Graduate Program in European Studies (LM-90) and the Master in Competition and Regulation of Markets (CORE), while also holding roles at international institutions like Zhongnan University of Economics and Law in China. Education: Ph.D. in Law and Economics, University of Siena M.Sc. in Economics, University of York, UK Master in Law and Economics, University of Siena Research Focus: Public Finance Law and Economics Environmental Crime Analysis Occupational Safety and Health Competition Policy Technological Innovation Impact His recent publications analyze workplace accident determinants, environmental crime drivers, and taxation policies for sin goods, employing empirical methods across European and Italian contexts. Current research projects focus on technology's impact on occupational safety and public investment incentives for workplace health improvements. He actively participates in academic conferences and serves as co-managing editor for public finance working papers at Sapienza University of Rome.
Zhandong Liu is an Associate Professor at Baylor College of Medicine with joint appointments in the Department of Pediatrics and Department of Neurology . He serves as Chief of Computational Sciences at Texas Children's Hospital and co-directs the Quantitative & Computational Biosciences Graduate Program at Baylor. Education: B.S. in Computer Science, Nankai University (2001) M.S. in Computer Science, Wayne State University (2003) Ph.D. in Genomics and Computational Biology, University of Pennsylvania (2010) Dr. Liu's research integrates genomics , machine learning , and bioinformatics to advance understanding of neurological diseases. His work focuses on: Multi-omics data integration for disease mechanism discovery Development of cloud-based CRISPR analysis tools like CRISPRcloud Augmented reality platforms for biomedical data visualization Identification of disease genes through computational models Alternative splicing analysis in cancer and neurodegeneration Single-cell and spatial transcriptomics algorithms His recent publications emphasize Alzheimer's disease , MECP2 syndromes , and computational therapy prediction across multiple domains. Scientific awards include the 2018 Outstanding Service Award from the International Association for Intelligent Biology and Medicine. He has secured major grants from NIH, CPRIT, and NSF for projects including: NSF grant #199977 (2018-2020): Augmented reality therapy platforms CPRIT grant #RP170387 (2016-2019): Network-guided cancer analysis NIH #1R01AG057339 (2017-2022): Alzheimer's disease networks As head of the Liu Lab , he leads teams developing tools like: MARRVEL : Human-model organism gene variant integration CRISPRcloud : Secure CRISPR screen analysis platform CrypSplice : Cryptic splicing detection algorithm
Professor Rafal Bogacz is a leading academic at the University of Oxford, affiliated with St Edmund Hall and the MRC Brain Network Dynamics Unit . He teaches computational neuroscience and statistics at both undergraduate and postgraduate levels, including the MSc in Neuroscience and BSc in Biomedical Science programs. MSc: Wroclaw University of Technology PhD: University of Bristol Postdoctoral Researcher: Princeton University His research focuses on computational neuroscience , particularly modeling brain networks involved in action selection , decision making , and Parkinson's disease pathophysiology. Key themes include: Developing predictive coding models of cortical computations Understanding basal ganglia neural circuits in healthy and diseased states Designing closed-loop deep brain stimulation paradigms Recent publications highlight work in neural plasticity , dopamine signaling , and computational psychiatry , with a notable Wellcome Discovery Award supporting research on learning in neurons . The Bogacz Group maintains strong collaborations with experimental neuroscientists and shares open datasets through the MRC BNDU Data Sharing Platform . Wellcome Discovery Award (2025): For learning in neurons Europe PMC Open Access (multiple): For numerous PLoS, Nat Neurosci, and J Neural Eng publications As a computational neuroscientist, Professor Bogacz supervises D.Phil. students and leads research programs that bridge theoretical neuroscience with clinical applications . The group actively participates in MRC BNDU training initiatives and public engagement activities like Schools Open Day demonstrations.
Pixu Shi is an Assistant Professor in the Department of Biostatistics and Bioinformatics at Duke University's School of Medicine. Previously, they served as a Visiting Assistant Professor in the Department of Statistics at the University of Wisconsin-Madison (2018-2020) and as a Postdoctoral Researcher in the Department of Biostatistics at the University of Wisconsin-Madison (2016-2018). Dr. Shi earned their PhD in Biostatistics from the University of Pennsylvania in 2016 under advisor Hongzhe Li. They also hold an MS in Biostatistics from the University of Pennsylvania (2015), an MS in Statistics from Rutgers University (2012), and a BS in Statistics from Peking University (2010). Dr. Shi's research focuses on developing statistical methods for Microbiome Research, Longitudinal/Temporal Omic Data analysis, Integration of Omic Data, Spatial Omics, and High-dimensional Statistical Inference. Their work bridges statistical theory with practical applications in biomedical research, particularly in microbiome studies where they've made significant contributions with the TEMPTED (TEMPoral TEnsor Decomposition) method. The article trends show a strong focus on microbiome analysis, statistical methodology development, and applications in obesity, infectious disease, and cancer research. Their most recent work (2024-2025) demonstrates expertise in tensor decomposition methods, longitudinal data analysis, and integrating microbiome data with clinical outcomes across diverse areas including adolescent obesity, viral infections, and cancer metastases. Dr. Shi has secured multiple substantial research grants from major institutions including the National Institutes of Health (NIMH, NIAID, NCI, NIDDK, NIA), totaling over a decade of continuous funding for projects related to microbiome research, HIV/AIDS, cancer biomarkers, and metabolic studies. They actively contribute to education through teaching courses such as BIOSTAT 905: Linear Models and Inference at Duke University and previously taught statistics courses at the University of Wisconsin-Madison. Dr. Shi has also organized specialized workshop series including Quantitative Methods for HIV/AIDS, Microbiome, Immunology, and Cancer Bioinformatics.
Anru Zhang is the tenured Eugene Anson Stead, Jr. M.D. Associate Professor with joint appointments in Biostatistics & Bioinformatics, Computer Science, Electrical and Computer Engineering, and Statistical Science at Duke University. He holds a Ph.D. from the University of Pennsylvania (2015, advised by T. Tony Cai) and a B.S. in Mathematics from Peking University (2010). Current roles: Associate Professor at Duke (2024–present), previously Assistant Professor at UW-Madison (2018–2021) Research focus: Tensor learning, high-dimensional statistics, EHR analysis, and healthcare applications Mentorship: Supervises active research team including postdocs (Jianbin Tan, Qiuyi Wu) and PhD students (Runshi Tang, Yinrui Sun) Research Trends : His recent publications emphasize tensor methods in biomedical data (EHR, microbiome, Alzheimer’s), Riemannian optimization for high-dimensional problems, and hybrid statistical-computational approaches. Key themes include healthcare AI, EHR analysis, and non-convex optimization. Scientific Awards : COPSS Emerging Leader Award (2024) IMS Tweedie New Researcher Award (2022) ASA Gottfried E. Noether Junior Award (2021) NSF CAREER Award (2020) AMIA Data Science Outstanding Paper Award (2023) Advising & Grants : Mentored 16+ students/postdocs, including Yuetian Luo (IMS Lawrence D. Brown Award) and Yuchen Zhou (IMS Hannan Travel Award). Current grants include NIH-funded projects on sepsis detection, mental health AI, precision genetic testing, and telehealth interventions, plus NSF CAREER funding for statistical inference in high-dimensional structures. Labs & Teams : Leads a research group at Duke focusing on tensor learning, statistical theory, and healthcare AI applications. Collaborates with Duke’s AI Health initiative and serves as Associate Editor for leading journals like Annals of Statistics and JASA.
Dr. Miguel Rico-Ramirez serves as Associate Professor of Radar Hydrology and Hydroinformatics at the University of Bristol's School of Civil, Aerospace and Design Engineering. His research integrates advanced radar technology with hydrological modeling to address critical water resource challenges including flood forecasting, drought management, and precipitation measurement across diverse global contexts from South Korea to Mexico City. Education: Bachelor of Engineering (Eng.) Master of Engineering (M.Eng.) Ph.D. in Engineering, University of Bristol His research program focuses on radar-based precipitation estimation, hydroinformatics, and flood prediction systems. He pioneers deep learning applications for rainfall nowcasting and develops innovative methods for uncertainty quantification in hydrological modeling. Current work emphasizes cosmic-ray neutron sensor validation, satellite-based flood mapping, and seasonal forecast applications for reservoir operations, with strong emphasis on translating research into operational water management solutions. Recent publications (2023-2025) reveal three dominant research thrusts: (1) deep learning frameworks for spatiotemporal rainfall prediction, (2) global validation of precipitation and soil moisture datasets using novel sensor networks, and (3) operational implementation of seasonal forecasts for drought mitigation in South Korea. His work consistently bridges radar meteorology with practical hydrological applications across urban and data-scarce environments. Scientific Awards: No specific awards documented in source materials Dr. Rico-Ramirez supervises postgraduate researchers in radar hydrology and hydroinformatics, with projects spanning flood early warning systems, precipitation nowcasting, and climate adaptation strategies. His research receives funding for international collaborations focused on water security challenges, particularly in drought-prone regions and data-scarce basins like the Nile Delta. Current grants support development of integrated forecasting systems combining global datasets with machine learning for extreme event management. He leads the Radar Hydrology research group within Bristol's Water and Environmental Engineering division, collaborating closely with Professor Dawei Han on hydroinformatics and Dr. Rafael Rosolem on water-climate interactions. The team maintains active partnerships with meteorological agencies and water authorities globally, particularly in flood forecasting system implementation across South Korea and Mexico.
Paul D. Brooks is a Professor in the Department of Geology/Geophysics at the University of Utah, where he has been a faculty member since July 2014. His research focuses on understanding water, energy, and biogeochemical cycling in seasonally snow-covered catchments, with increasing emphasis on predicting how climate and land use changes impact snow accumulation, ablation, and snowmelt-derived surface and ground water resources. His educational background includes a BS in Biology and Chemistry from Florida State University, followed by an MS in Ecohydrology (1991) and PhD in Biogeochemistry (1995), both from the University of Colorado, Boulder. Prior to his position at the University of Utah, Dr. Brooks was a Professor in the Department of Hydrology and Water Resources at the University of Arizona from December 2000 to June 2014. Dr. Brooks' research spans multiple disciplines within earth sciences, focusing primarily on hydrology, ecohydrology, and biogeochemical cycling in mountainous, snow-dominated environments. His work examines how climate change affects snowmelt processes, groundwater-surface water interactions, and water resource availability in the western United States. He employs a combination of field measurements, isotope hydrology, and modeling approaches to understand complex hydrological processes across multiple spatial and temporal scales. His research increasingly involves collaboration with stakeholders to translate scientific findings into practical water resource management applications. Analysis of Dr. Brooks' recent publications reveals a strong focus on groundwater-surface water interactions in snowmelt-dominated systems, with particular attention to how climate change affects streamflow generation processes. His work bridges fundamental hydrological science with practical water resource concerns, examining topics such as runoff efficiency, groundwater storage dynamics, and the impacts of land cover changes on hydrological processes. A significant portion of his recent research investigates the Western United States water resources under changing climate conditions. AGU Fellow (American Geophysical Union) Dr. Brooks actively mentors graduate students through thesis research (both PhD and Master's level) as evidenced by his teaching activities. His lab conducts research supported by various grants focused on understanding water resources in mountainous regions, particularly examining how climate change affects snowmelt hydrology and water availability. He collaborates extensively with researchers across multiple institutions, as demonstrated by his numerous co-authored publications with scientists from various universities and research organizations. Dr. Brooks leads research efforts through his lab at the University of Utah and is involved with the Wasatch Environmental Observatory, a mountain-to-urban research network in the semi-arid Western US. His work integrates field measurements across complex terrain to understand how topography, vegetation, and climate interact to control water, energy, and biogeochemical cycling in seasonally snow-covered environments.
Professor Vlad Mykhnenko is Professor of Geography and Political Economy in the University of Oxford Department for Continuing Education, and a Research Fellow in Sustainable Urban Development at St. Peter's College, Oxford. He serves as Academic Director for Social Sciences at Oxford Lifelong Learning and has been with the Department since January 2017, initially taking up an Associate Professorship in Sustainable Urban Development. His academic career spans multiple institutions including the University of Birmingham, Al-Farabi Kazakh National University, the University of Nottingham, the University of Glasgow, and the Central European University. Professor Mykhnenko's educational background includes: PhD in Political Economy from Darwin College, the University of Cambridge (2005) MA in International Relations and European Studies from the Central European University (1999) MA (1998) and BA (1996) in International Relations from Taras Shevchenko National University of Kyiv As an economic geographer specializing in geographical political economy, Professor Mykhnenko challenges conventional wisdom about urban and regional development. His research initially focused on transition economies of Eastern Europe (particularly Polish Upper Silesia and the Ukrainian Donbas) before expanding to cities and regions worldwide. He has produced over 140 research outputs and secured £16 million in external research funding, with £2 million as Principal Investigator. His current research initiatives focus on green steel - rebuilding Ukraine's iron and steel sector post-war without fossil fuels - and sustainable urban development in Kazakhstan. His publication record demonstrates significant scholarly impact across diverse areas including urban shrinkage, post-war reconstruction, geopolitical analysis, and future trends in technology and governance. The breadth of his research interests spans from analyzing spatial economic patterns in African cities to examining the implications of the metaverse for future business practices. Professor Mykhnenko has substantial policy impact, with research quoted in at least 79 policy documents by 49 organizations across 21 countries. His work contributes to UN Sustainable Development Goals, with 30% directly attributed to SDG11: Sustainable Cities and Communities. He has advised governments, intergovernmental organizations, and multinational enterprises, including serving on a UN expert group to shape the New Urban Agenda and providing expert evidence to the UK House of Commons on Ukraine's recovery. In early 2025, he was appointed a commissioner for the Lancet Commission on Ukraine. Professor Mykhnenko has taught over 3,100 students across various institutions and currently contributes to Oxford's MSc in Sustainable Urban Development program. He has supervised numerous doctoral students and received recognition for his teaching excellence, including Fellow of the Higher Education Academy status and a Postgraduate Certificate in Academic Practice. His commitment to research-led teaching has resulted in innovative pedagogical approaches documented in peer-reviewed academic publications.
Deepak R Mishra is the Merle C. Prunty, Jr. Professor of Geography at the University of Georgia, where he serves as Professor and Head of the Department of Geography. He directs both the Center for Geospatial Research (CGR) and the UGA Small Satellite Research Laboratory (SSRL), demonstrating leadership across multiple significant research initiatives. His academic career spans geospatial science, remote sensing applications, and environmental monitoring with particular focus on coastal ecosystems. Dr. Mishra earned his PhD in Natural Resources from the University of Nebraska, Lincoln in 2006 and completed his M. Tech in Civil Engineering at the Indian Institute of Technology, Kanpur in 2002. His research focuses on combining field-based remote sensing with satellite technologies to monitor and study coastal and inland water resources. His work addresses critical environmental challenges including harmful algal blooms, salt marsh conservation, carbon sequestration in tidal wetlands, and sea level rise impacts. Dr. Mishra's research portfolio reveals strong trends in geospatial analytics for environmental monitoring, with increasing integration of artificial intelligence and small satellite technologies. His recent publications show a growing emphasis on climate change impacts on coastal ecosystems, particularly salt marsh vulnerability and carbon sequestration capacity. The work demonstrates innovative applications of remote sensing for tracking cyanobacterial blooms and developing early warning systems through platforms like CyanoTRACKER. Outstanding Service Award, SEDAAG 2019 Creative Research Medal, University of Georgia, 2017 Best Poster Award, TROPMET 2016, India Article ranked #4 in Altmetric Attention Score in Nature Climate Change 2016 Mississippi State University Faculty Research Award (2012) GRI Academic Faculty of the year (2011) Dr. Mishra has secured substantial research funding, including a $7.5 million NSF LTER grant and a $4.7 million Army Research Lab grant for autonomous navigation systems. His lab actively mentors graduate students including Tyler Lynn, Lishen Mao, and Chintan Maniyar, who contribute to projects spanning coastal monitoring, small satellite development, and AI applications. The Small Satellite Research Laboratory recently achieved a historic milestone with the launch of SPOC satellite to the International Space Station, marking UGA's first satellite deployment.
Professor Evan Morris is a faculty member at Yale School of Medicine in the Radiology and Biomedical Imaging department, with secondary appointments in Biomedical Engineering and Psychiatry. His work combines advanced kinetic modeling and dynamic PET imaging to visualize neurotransmitter dynamics in the brain. PhD in Chemical Engineering from Case Western Reserve University (1991) Postdoctoral fellowship at Massachusetts General Hospital (1995) Morris specializes in creating "dopamine movies" through PET imaging, revealing transient neurochemical fluctuations related to addiction, Parkinson's disease, and stress responses. His group develops novel parametric imaging methods for faster drug discovery and disease biomarker identification. Recent publications focus on: Nonsteady-state PET modeling Stress-induced opioid receptor connectivity Exercise effects on Parkinson's neuropathology EC50 image quantification Scientific recognitions include: Fulbright Senior Scholar (2015) Yale Graduate Mentor of the Year (2013) He serves as Principal Investigator for studies on nicotine addiction and collaborates across Yale's Morris Lab , PET Core , and Neural Disorders programs, advancing applications in Medical Imaging , Neurochemistry , and Biomedical Engineering .
Anton Rozhkov is an Industry Assistant Professor and Director of the M.S. in Applied Urban Science and Informatics Program at the Center for Urban Science and Progress (CUSP) at New York University (NYU) Tandon School of Engineering. His work focuses on applying geospatial tools, modeling techniques, and data science to address complex challenges in urban environments, with particular emphasis on infrastructure planning and city design. Dr. Rozhkov earned his Ph.D. in Urban Planning and Policy from the University of Illinois Chicago, where his research centered on decentralized and renewable energy systems in urban contexts through a complex systems approach. Prior to his doctoral studies, he received an M.S./B.S. in Engineering in Land Cadaster from the State University of Land Use Planning in Moscow, Russia, and worked as a senior specialist in the Russian power grid sector with "Rosseti" Group of Companies. His research interests span the application of complex systems, data science, and spatial analytics to solve urban challenges, particularly focusing on how data-driven policies and new technologies can transform infrastructure planning and city design. Dr. Rozhkov employs methods including causal loop diagrams, system dynamics, and agent-based modeling to understand how decentralized energy systems interact with existing power grids and contribute to sustainable urban development. He has published extensively on urban transportation, energy systems, and census data analysis, with a notable focus on Chicago's urban landscape and Illinois state initiatives. Dr. Rozhkov has been actively involved in several significant research projects including an empirical investigation into affordable transit-oriented development in California sponsored by the California State University Transportation Consortium, the Sustainable Urban-Regional Modeling Network project funded by the Illinois Innovation Network, and the Census 2020 Map-The-Count project with the Illinois Department of Human Services which developed predictive models for census response rates and a GIS platform for reporting outreach activities. Ph.D. in Urban Planning and Policy, University of Illinois Chicago M.S./B.S. in Engineering in Land Cadaster, State University of Land Use Planning (Moscow, Russia) His teaching portfolio includes courses on geographic information systems (GIS), advanced spatial analysis, decision modeling, and machine learning for cities. Dr. Rozhkov emphasizes not just understanding urban trends but exploring the "why" behind these trends to develop sustainable solutions. His recent publications (2020-2025) demonstrate a consistent research trajectory examining the complex interrelationships between urban infrastructure systems, particularly focusing on energy, transportation, and spatial patterns through sophisticated analytical methods. Outside of his academic work, Dr. Rozhkov is passionate about urban and landscape photography, traveling, running, snowboarding, and playing guitar. He was born and raised in Balashikha, a city in the Moscow suburbs in Russia, and maintains a gallery of his photographic work from various global locations.
